code: def test_mixed_dtype():
      algorithm = GPyOpt(max_num_trials=4)
      parameters = [
          sherpa.Choice("param_int", [0, 1]),
          sherpa.Choice("param_float", [0.1, 1.1]),
      ]
      study = sherpa.Study(
          parameters=parameters,
          algorithm=algorithm,
          lower_is_better=True,
          disable_dashboard=True,
      )
      for trial in study:
          study.add_observation(trial, iteration=0, objective=0)
          study.finalize(trial)
      assert type(trial.parameters["param_int"]) == int
      assert type(trial.parameters["param_float"]) == float
  
imports: import sherpa
  import pandas
  import pytest
  import numpy
  import collections
  import GPyOpt
  from sherpa.algorithms.bayesian_optimization import GPyOpt
  from sherpa.algorithms import Repeat
label_desc: Use image node_id 22 for calling a global function with example usage: test_mixed_dtype() without return types